/*
 * Copyright 2016 Red Hat, Inc. and/or its affiliates.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.optaplanner.training.election.solver;

import org.optaplanner.core.api.score.Score;
import org.optaplanner.core.api.score.buildin.hardsoft.HardSoftScore;
import org.optaplanner.core.impl.score.director.easy.EasyScoreCalculator;
import org.optaplanner.training.election.domain.Election;
import org.optaplanner.training.election.domain.FederalState;

public class ElectionEasyScoreCalculator implements EasyScoreCalculator<Election> {

    @Override
    public Score calculateScore(Election election, int initScore) {
        int gamerCandidateWins = 0;
        int gamerMinimumPopulation = 0;
        for (FederalState federalState : election.getFederalStateList()) {
            if (Election.GAMER_CANDIDATE.equals(federalState.getWinningCandidate())) {
                gamerCandidateWins += federalState.getElectoralVotes();
                gamerMinimumPopulation += federalState.getMinimumMajorityPopulation();
            }
        }
        int hardScore = (gamerCandidateWins >= 270) ? 0 : (gamerCandidateWins - 270);
        return HardSoftScore.valueOf(initScore, hardScore, -gamerMinimumPopulation);
    }

}
